# tools/forecaster.py
# ------------------------------------------------------------
# Fits an ARIMA(1,1,1) model to any (date, value) series,
# forecasts the next `periods` steps, plots history + forecast,
# and saves a hi‑res PNG copy to /tmp (or custom output_dir).

import os
import tempfile
from typing import Tuple, Union

import pandas as pd
import plotly.graph_objects as go
from statsmodels.tsa.arima.model import ARIMA

# Typing alias
Plot = go.Figure


def forecast_metric_tool(
    file_path: str,
    date_col: str,
    value_col: str,
    periods: int = 3,
    output_dir: str = "/tmp",
) -> Union[Tuple[pd.DataFrame, str], str]:
    """
    Parameters
    ----------
    file_path : str
        CSV or Excel path.
    date_col  : str
        Column to treat as the date index.
    value_col : str
        Numeric column to forecast.
    periods   : int
        Steps ahead to forecast.
    output_dir: str
        Directory to save PNG.

    Returns
    -------
    (forecast_df, png_path)   on success
    error string (starting '❌') otherwise
    """
    # ── 1. Load file ──────────────────────────────────────────
    ext = os.path.splitext(file_path)[1].lower()
    try:
        df = (
            pd.read_excel(file_path)
            if ext in (".xls", ".xlsx")
            else pd.read_csv(file_path)
        )
    except Exception as exc:
        return f"❌ Failed to load file: {exc}"

    # ── 2. Column validation ─────────────────────────────────
    missing = [c for c in (date_col, value_col) if c not in df.columns]
    if missing:
        return f"❌ Missing column(s): {', '.join(missing)}"

    # ── 3. Parse & clean ─────────────────────────────────────
    df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
    df[value_col] = pd.to_numeric(df[value_col], errors="coerce")
    df = df.dropna(subset=[date_col, value_col])
    if df.empty:
        return f"❌ No valid data after cleaning '{date_col}' / '{value_col}'."

    # Aggregate duplicate timestamps → mean
    df = (
        df[[date_col, value_col]]
        .groupby(date_col, as_index=True)
        .mean()
        .sort_index()
    )

    # Infer or default frequency
    freq = pd.infer_freq(df.index) or "D"
    try:
        df = df.asfreq(freq)
    except Exception:
        # fallback if duplicates still exist
        df = (
            df[~df.index.duplicated(keep="first")]
            .asfreq(freq)
        )

    # ── 4. Fit ARIMA(1,1,1) ──────────────────────────────────
    try:
        model = ARIMA(df[value_col], order=(1, 1, 1))
        fit = model.fit()
    except Exception as exc:
        return f"❌ ARIMA fitting failed: {exc}"

    # ── 5. Forecast ──────────────────────────────────────────
    try:
        pred = fit.get_forecast(steps=periods)
        forecast = pred.predicted_mean
    except Exception as exc:
        return f"❌ Forecast generation failed: {exc}"

    forecast_df = forecast.to_frame(name="Forecast")

    # ── 6. Plot history + forecast ───────────────────────────
    fig: Plot = go.Figure()
    fig.add_scatter(x=df.index, y=df[value_col], mode="lines", name="History")
    fig.add_scatter(
        x=forecast.index, y=forecast, mode="lines+markers", name="Forecast"
    )
    fig.update_layout(
        title=f"{value_col} Forecast",
        xaxis_title=date_col,
        yaxis_title=value_col,
        template="plotly_dark",
    )

    # ── 7. Save PNG ──────────────────────────────────────────
    os.makedirs(output_dir, exist_ok=True)
    tmp_png = tempfile.NamedTemporaryFile(
        prefix="forecast_", suffix=".png", dir=output_dir, delete=False
    )
    png_path = tmp_png.name
    tmp_png.close()
    try:
        fig.write_image(png_path, scale=2)
    except Exception as exc:
        return f"❌ Plot saving failed: {exc}"

    return forecast_df, png_path